class_docstr: Flask Dynamic Retrieval server, which helps to build dynamic retrieval index.
code: def __init__(
      self,
      faiss_devices: str,
      tokenizer: TokenizerSpec,
      chunk_size: int = 64,
      stride: int = 32,
      faiss_index: str = None,
      store_file: str = None,
      ctx_bert_ip: str = None,
      ctx_bert_port: int = 0,
      query_bert_ip: str = None,
      query_bert_port: int = 0,
      output_filename: str = "dynamic_db",
  ):
      self.app = Flask(__name__, static_url_path="")
      has_gpu = torch.cuda.is_available() and hasattr(
          faiss, "index_gpu_to_cpu"
      )
      embedding_dim = request_data({}, ctx_bert_ip, ctx_bert_port)[
          "dim"
      ]
  
      if faiss_index is not None:
          self.index = faiss.read_index(faiss_index)
      else:
          self.index = faiss.IndexFlatL2(
              embedding_dim
          )  # build the index
      self.pad_id = tokenizer.pad_id
      self.chunk_size = chunk_size
      self.stride = stride
      if store_file is not None:
          with open(store_file, "rb") as f:
              self.store = pickle.load(f)
      else:
          self.store = ChunkStore(chunk_size, self.pad_id)
  
      if faiss_devices is None or not torch.cuda.is_available():
          device_list = None
      else:
          device_list = [
              "cuda:" + str(device)
              for device in faiss_devices.split(",")
          ]
  
      if has_gpu and device_list is not None:
          beg = time.time()
          co = faiss.GpuMultipleClonerOptions()
          co.useFloat16 = True
          co.usePrecomputed = False
          co.shard = True
          self.index = faiss.index_cpu_to_all_gpus(
              self.index, co, ngpu=len(device_list)
          )
          end = time.time()
          logging.info(f"convert Faiss db to GPU takes {end - beg} s")
  
      self.tokenizer = tokenizer
  
      api = Api(self.app)
      api.add_resource(
          DynamicRetrievalResource,
          "/knn",
          resource_class_args=[
              self.index,
              self.tokenizer,
              self.chunk_size,
              self.stride,
              self.store,
              ctx_bert_ip,
              ctx_bert_port,
              query_bert_ip,
              query_bert_port,
              output_filename,
          ],
      )
  
imports: import base64
  import logging
  import pickle
  import threading
  import time
  from collections import namedtuple
  from typing import List
  import faiss
  import numpy
  import torch
  from flask import Flask, jsonify, request
  from flask_restful import Api
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.static_retrieval_server import FaissRetrievalResource
  from nemo.collections.nlp.modules.common.megatron.retrieval_services.util import lock, request_data
label_desc: Use image node_id 1 to create a new DynamicRetrievalServer object from inherited base classes: object with example: obj = DynamicRetrievalServer(faiss_devices, tokenizer, chunk_size, stride, faiss_index, store_file, ctx_bert_ip, ctx_bert_port, query_bert_ip, query_bert_port, output_filename)